References
- Aggarwal P.K., Romatschke U., Araguas-Araguas L., Belachew D., Long-staffe F.J., Berg P., Schumacher C., Funk A. 2016. Proportions of convective and stratiform precipitation revealed in water isotope ratios. Nature Geoscience, 9, 624–629. https://doi.org/10.1038/ngeo2739
- Benischke R., Brielmann H., Dalla-Via A., Goldbrunner J., Grath J., Harum T., Humer F., Kralik M., Leis A., Philippitsch R., Rank D., Reszler C., Schott K., Wemhöhner U., Wyhlidal S., 2018. Isotopenzusammensetzung in natürlichen Wässern in Österreich – Grundlagen und Anwendungsbeispiele zur Wasser-Isotopenkarte Österreichs 1:500 000, 154, Bundesministerium für Nachhaltigkeit und Tourismus, Vienna. https://info.bml.gv.at/dam/jcr:eba9348f-cefb-465f-94abfbbe444c9046/Wasser-Isotopenkarte_Bericht_final.pdf
- BGBl. (Bundesgesetzblatt), 2006. Ordinance about the monitoring of the water Quality of the Austrian Federal Ministry of Sustainability and Tourism (BMNT), offices of provincial governments (Gewässerzustandsüberwachungsverordnung-GZÜV). II. Nr. 479, Vienna.
- Bowen G.J., Good S.P., 2015. Incorporating water isoscapes in hydro-logical and water resource investigations. WIREs Water, 2, 107–119. https://doi.org/10.1002/wat2.1069
- Bowen G.J., Putman A., Brooks J.R., Bowling D.R., Oerter E.J., Good S.P., 2018. Inferring the source of evaporated waters using stable H and O isotopes. Oecologia, 187, 1025–1039. https://doi.org/10.1007/s00442-018-4192-5
- Di Cecco G.J., Gouhier T.C., 2018. Increased spatial and temporal autocorrelation of temperature under climate change. Scientific Reports, 8, 14850. https://doi.org/10.1038/s41598-018-33217-0
- Cervi F., Borgatti L., Dreossi G., Marcato G., Michelini M., Stenni B., 2017. Isotopic features of precipitation and groundwater from the Eastern Alps of Italy: Results from the Mt. Tinisa hydrogeological system. Environmental Earth Sciences, 76, 410. https://doi.org/10.1007/s12665-017-6748-9
- Coplen T.B., 1994. Reporting of stable hydrogen, carbon and oxygen isotopic abundances. Pure and Applied Chemistry, 66, 273–276. https://doi.org/10.1351/pac199466020273
- Coplen T.B., Herczeg A.L., Barnes C., 2000. Isotope Engineering - Using Stable Isotopes of the Water Molecule to Solve Practical Problems. In: Cook P.G., Herczeg A.L. (eds.), Environmental Tracers in Sub-surface Hydrology. Springer US, Boston, MA, 79–110. https://doi.org/10.1007/978-1-4615-4557-6_3
- Coplen T.B., Qi H., 2009. Quality assurance and quality control in light stable isotope laboratories: A case study of Rio Grande, Texas, water samples. Isotopes in Environmental and Health Studies, 45, 126–134. https://doi.org/10.1080/10256010902871952
- Dansgaard W., 1964. Stable isotopes in precipitation. Tellus, 16, 436–468. https://doi.org/10.3402/tellusa.v16i4.8993
- Erdélyi D., Kern Z., Hatvan I.G., Vreča P., Žagar K., Huneau F., Perșoiu A., Leuenberger M., Lojen S., Kracht O., Harjung A., Rossi P., Mustonen K.-R., Welker J., 2024a. Machine learning analysis for predicting spatial distribution and key influencers of stable isotope patterns in European precipitation. EGU General Assembly. Vienna, Austria, 14–19 April 2024, EGU24-15660. https://doi.org/10.5194/egusphereegu24-15660
- Erdélyi D., Kern Z., Hatvan I.G., Vreča P., Žagar K., Huneau F., Perșoiu A., Leuenberger M., Lojen S., Kracht O., Harjung A., Rossi P., Mustonen K.-R., Welker J., 2024b. Predicting the spatial distribution of stable isotopes in monthly precipitation across Europe using a machine learning approach. 12th International Geostatistics Congress, Geo-statistics Congress, Ponta Delgada, Portugal.
- Erdélyi D., Kern Z., Nyitrai T., Hatvani I.G., 2023. Predicting the spatial distribution of stable isotopes in precipitation using a machine learning approach: A comparative assessment of random forest variants. GEM - International Journal on Geomathematics, 14, 14. https://doi.org/10.1007/s13137-023-00224-x
- Eshel A., Alpert P., Messer H., 2022. Estimating the Parameters of the Spatial Autocorrelation of Rainfall Fields by Measurements From Commercial Microwave Links IEEE Transactions on Geo-science and Remote Sensing 60, 1–11. https://doi.org/10.1109/TGRS.2022.3165309
- Fórizs I., 2003. Isotopes as natural tracers in the water cycle: Examples from the Carpathian Basin. Studia UBB Physica, 48, 69–77.
- Fórizs I., 2005. Processes behind the isotopic water line: water cycle and climate Studia UBB Physica, 50, 138–146.
- Gat J.R., 2005. Some classical concepts of isotope hydrology. In: Aggarwal P.K., Gat J.R., Froehlich K.F. (eds.), Isotopes in the Water Cycle: Past, Present and Future of a Developing Science. Springer Netherlands, Dordrecht, 127–137. https://doi.org/10.1007/1-4020-3023-1_10
- Hager B., Foelsche U., 2015. Stable isotope composition of precipitation in Austria. Austrian Journal of Earth Sciences, 108, 2–14. https://doi.org/10.17738/ajes.2015.0012
- Hatvani I.G., Szatmári G., Kern Z., Erdélyi D., Vreča P., Kanduč T., Czuppon Gy., Lojen S., Kohán B., 2021. Geostatistical evaluation of the design of the precipitation stable isotope monitoring network for Slovenia and Hungary Environment International, 146, 106263. https://doi.org/10.1016/j.envint.2020.106263
- IAEA 1992. Statistical treatment of data on environmental isotopes in precipitation. Technical Report Series, Vol. 331. International Atomic Energy Agency, Vienna.
- IAEA 2023. Global Network of Isotopes in Precipitation. The GNIP Database. https://nucleus.iaea.org/wiser/explore/
- Kern Z., Hatvani I.G., Czuppon G., Fórizs I., Erdélyi D., Kanduč T., Palcsu L., Vreča P., 2020. Isotopic ‘Altitude’ and ‘Continental’ effects in modern precipitation across the Adriatic-Pannonian region. Water, 12, 1797. https://doi.org/10.3390/w12061797
- Knorr E.M, Ng R.T, Tucakov V., 2000. Distance-based outliers: algorithms and applications The VLDB Journal 8, 237–253. https://doi.org/10.1007/s007780050006
- Kralik M., Papesch W., Stichler W., 2003. Austrian Network of Isotopes in Precipitation (ANIP): Quality assurance and climatological phenomenon in one of the oldest and densest networks in the world. Isotope Hydrology and Integrated Water Resources Management. 146–149.
- Kralik M., Terzer S., Wyhlidal S., 2015. Vienna GNIP-ANIP-station: a unique station with 40 years of dual measurements. In International Symposium on Isotope Hydrology, 104–106.
- Landwehr J., Coplen T., 2006. Line-conditioned excess: a new method for characterizing stable hydrogen and oxygen isotope ratios in hydrologic systems. In: International conference on isotopes in environmental studies, Monaco, vol 56. IAEA Vienna, Vienna, Austria, 132–135.
- Liu J., Song X., Yuan G., Sun X., Yang L., 2014. Stable isotopic compositions of precipitation in China Tellus B: Chemical and Physical Meteorology, 66, 22567. https://doi.org/10.3402/tellusb.v66.22567
- Mellat M., Bailey H., Mustonen K.-R., Marttila H., Klein E.S., Gribanov K., Bret-Harte M.S., Chupakov A.V., Divine D.V., Else B., Filippov I., Hyöky V., Jones S., Kirpotin S.N., Kroon A., Markussen H.T., Nielsen M., Olsen M., Paavola R., Pokrovsky O.S., Prokushkin A., Rasch M., Raundrup K., Suominen O., Syvänperä I., Vignisson S.R., Zarov E., Welker J.M., 2021. Hydroclimatic controls on the isotopic (δ18O, δ2H, d-excess) traits of pan-Arctic summer rainfall events. Frontiers in Earth Science, 9, 651731. https://doi.org/10.3389/feart.2021.651731
- Muhr D., Affenzeller M., 2022. Little data is often enough for distance-based outlier detection. Procedia Computer Science, 200, 984–992. https://doi.org/10.1016/j.procs.2022.01.297
- Nelson D.B., Basler D., Kahmen A., 2021. Precipitation isotope time series predictions from machine learning applied in Europe. Proceedings of the National Academy of Sciences, 118/26, e2024107118. https://doi.org/10.1073/pnas.2024107118
- Nigro M., Žagar K., Vreča P., 2024. A Simple Water Sample Storage Test for Water Isotope Analysis. Sustainability, 16, 4740. https://doi.org/10.3390/su16114740
- Putman A.L., Fiorella R.P., Bowen G.J., Cai Z., 2019. A Global Perspective on Local Meteoric Water Lines: Meta-analytic Insight Into Fundamental Controls and Practical Constraints. Water Resources Research, 55, 6896-6910 https://doi.org/10.1029/2019WR025181
- Rank D., Wyhlidal S., Heiss G., Papesch W., Schott K., 2016. Arsenal environmental-isotope laboratories 1964–2010: More than 45 years of production, application, and interpretation of isotope-hydrological data for Central Europe. Austrian Journal of Earth Sciences, 109, 4–28. https://doi.org/10.17738/ajes.2016.0001
- Rozanski K., Araguás-Araguás L., Gonfiantini R., 1993. Isotopic patterns in modern global precipitation. In: Swart P.K., Lohmann K.C., McKenzie J., Savin S. (eds.), Climate Change in Continental Isotopic Records. American Geophysical Union, USA, 1–36. https://doi.org/10.1029/GM078p0001
- Shekhar S., Lu C-T., Zhang P., 2003. A Unified Approach to Detecting Spatial Outliers. GeoInformatica, 7, 139–166. https://doi.org/10.1023/A:1023455925009
- Smiti A., 2020. A critical overview of outlier detection methods. Computer Science Review, 38, 100306. https://doi.org/10.1016/j.cosrev.2020.100306
- Vreča P., Pavšek A., Kocman D., 2022. SLONIP - A Slovenian Web-Based Interactive Research Platform on Water Isotopes in Precipitation. Water, 14, 2127. https://doi.org/10.3390/w14132127
- Wilcox R.R., 2003. Probability and related concepts, In: Wilcox R.R., (ed.) Applying Contemporary Statistical Techniques, Academic Press, 17–53. https://doi.org/10.1016/B978-012751541-0/50023-7
- Wilkinson M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton M., Baak A., Blomberg N., Boiten J.W., da Silva Santos L.B., Bourne P.E., Bouwman J., Brookes A.J., Clark T., Crosas M., Dillo I., Dumon O., Edmunds S., Evelo C.T., Finkers R., Gonzalez-Beltran A., Gray A.J.G., Growth P., Goble C., Grethe J.S., Heringa J., ‘t Hoen P.A.C., Hooft R., Kuhn T., Kok R., Kok J., Lusher S.J., Martone M.E., Mons A., Packer A.L., Persson B., Rocca-Serra P., Roos M., van Schaik R., Sansone S.A., Schultes E., Sengstag T., Slater T., Strawn G., Swertz M.A., Thompson M., van der Lei J., van Mulligen E., Velterop J., Waagmeester A., Wittenburg P., Wolstencroft K., Zhao J., Mons B., 2016. The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data 3, 160018. https://doi.org/10.1038/sdata.2016.18
- Wu E., Liu W., Chawla S., 2010. Spatio-temporal Outlier Detection in Precipitation Data. In: Gaber M.M., Vatsavai R.R., Omitaomu O.A., Gama J., Chawla N.V., Ganguly A.R. (eds.), Knowledge Discovery from Sensor Data. Springer, Berlin, Heidelberg, 115–133. https://doi.org/10.1007/978-3-642-12519-5_7